New NN methods have been introduced to extract the most meaningful vectors from time series.
Long short-term memory (LSTM) improves recurrent NNs (RNNs), enabling them to retain meanings extracted from more distant time points [1]}.
Other studies have attempted to compensate for the shortcomings of LSTM [2]}, [3]}, [4]}.
Meanwhile, attention-based and convolutional NNs (CNNs) have been applied to time-series predictions, showing good performance [5]}, [6]}.
The bidirectional-encoder-representations-from-transformers (BERT) model with an attention layer is among the most powerful natural language processing (NLP) tools used today [7]}.
Notably, the positional encoding used by BERT to convey location information in the attention layer is effective for time-series learning [8]}.
The Informer application achieved state-of-the-art (SOTA) performance with time-series forecasting by reducing the computational costs of the attention layer and improving positional encoding [9]}.
A temporal CNN using dilated convolution is a representative model that processes time-series based on a CNN [10]}, [11]}.
Recently, SCINet achieved SOTA on benchmark datasets by convolving a binary tree-like hierarchical structure [12]}.
